This series highlights the key insights and lessons from our Digital Leadership series of podcasts. We spotlight the important takeaways from our interviews in an accessible format. The following insights come from Jon Sobel, Co-Founder and CEO of Sight Machine. Stay tuned for the full podcast interview with Jon, in the meantime, take a look at our full library of podcasts.
I know you’ve got an interesting background having worked for some very prominent companies. I would love to hear about what’s brought you to where you are today.
My technology career started in the mid-nineties, I started working with a semiconductor company doing graphics chips in the PC era. That led to a stint at Yahoo! where I didn’t look up for about six years, I joined in the late nineties and when I left I was on the management team there. It was a really interesting time because the first chapter of the internet was coming to a close, and you could tell where Google was headed and what was about to happen next.
I then went to a large media company, CBS when online video and mobile was happening, I wanted to see what it was like to be on the incumbent side of the technology dynamic. I was there for a couple of years, I missed being on the disruptor side, and that’s where I met Nathan Oostendorp, the CTO of our company and the Founder. He and I got to know each other at a precursor to GitHub, an open source distribution platform that housed a storage website called Slashdot, and a large open source distribution platform called SourceForge.
I then got very interested in energy, I was briefly on the management team of Tesla, I also worked for a really interesting carbon capture company and the takeaway from those experiences was, the opportunity for the intersection of high technology in traditional industry. It was very evident that there was going to be a lot of opportunity for companies that could talk to both sides of the street. At that point around 2011 Nate took the work we had done around data, processing huge amounts of it, and started to think about where to throw that. He contacted me when he had one other partner, one of our Founders, Anthony Oliver, and some help from another one of our founders, Kurt DeMaagd, we all started thinking about manufacturing in 2011!
I’d love to hear a bit about the backdrop and the origin story behind the genesis of Sight Machine; whether it be manufacturing or the pain points that you identified, where you saw that there was a need which hadn’t been addressed yet?
So, the origin of the company is, we decided that manufacturing was a really interesting domain because on first principles its appealing in a number of ways that people now appreciate but weren’t widely understood then. If we think of data as fuel for insight, how much data is there? Manufacturing has more data than any other category by a fact of two. So, it’s a domain with a lot of data, if we think about the economic value of the data, what is the percentage point of improvement in manufacturing work, in any large manufacturer’s worth tens of millions of dollars, maybe more? So, there’s good initial conditions from a technology point of view, a lot of data, there’s very compelling economic impact that’s quantifiable and meaningful. It’s a really hard area, and in 2011 there hadn’t been a lot of progress in 20 or 30 years in using this data.
We spent about two years, most of us were in SE Michigan and there’s a lot of automotive manufacturing, we went to a bunch of factories. We did what you should do when you start a company, we asked a lot of questions, we spent about two years going to a bunch of plants and asking, ‘Where is the pain?’ ‘What is the pain?’ And what we quickly realized was, plants were awash in data, but they couldn’t use it.
Like many start-ups we began with a very specific focus on a certain type of data. We started working with image data from machine vision systems, that’s not a widely known area, but what happens is there’s a lot of cameras in factories that take pictures apart that are being produced, the cameras generate a bunch of images, and we started to apply very sophisticated AI techniques to understand the images and identify variation. We got hired by a couple of really big companies, and what immediately happened was, they said, ‘It’s cool that you understand this, but we want you to understand everything at once, we’ve got all different kinds of data, and we’ve got a bunch of point solutions, we want something that understands it all’. So, about two to three years into this, that’s when we really locked into our opportunity.
Could you talk a bit about your technology, and then how that gets applied in the context of your customers manufacturing environments.
So, we’re a software company, the first thing I should say at the outset is we’re completely agnostic as to company’s edge situation of cloud strategy, we work on whatever edge networks they have and however they’re moving data. If they are moving data or however, we might extract it and aggregate it, all that is quite flexible. We’re a subscription software product, it’s a product that’s streams data as its being generated through a transformation layer. Then takes what you can think of as a manufacturing data warehouse and either pushes that date in the client systems that the client has, a factory information system or wants to use Tableau, or SaaS, or whatever great tools are out there for BI visualization, power BI like you can push it into all that stuff. We also have a browser-based visualization and analysis layer, and all this is very open.
So, a lot of times companies will start with visibility, they’ll be somewhat surprised by what surfaces as problems, they’ll dig into it, they’ll figure out what’s causing them, they’ll fix those problems and move onto harder ones. So, what we really end up doing with our client is, we offer this product, it brings a lot of new insight, and it ends up helping companies develop a workflow around data. So, we will work with them on developing that workflow, and really bringing this into production.
How would you compare the state of adoption of either advanced technology such as yours, across manufacturing?
My personal view is that it’s still really early, there’s been a palpable shift in the market in the last two years, most companies are spending money and trying stuff. If I hazard a guess and put a rough cut on it, maybe 1 in 10, or 1 in 7 or 8 are really serious, experienced, and thinking about scale. Scale is the big divide, just about every manufacturer is doing a bunch of POCs, but the problem is, you can test the technology, you can test it on the bench, but that’s not the same as scaling the technology.
I read a fascinating report last week from Morgan Stanley, and they said in their experience when 20 percent of an industry gets serious, that’s a tipping point. I don’t think we’re 20 percent, but we will be in a year or two, and there’s now fear in the market, it’s really interesting! When we started out it was all about hope, there were a couple of companies that thought, ‘This is really cool, I want to be a good visionary, an early adopter, I want to learn about this’. There is now palpable fear from companies that they’re going to be left behind their competitors, and that’s driving a lot of spending and engagement.
So, if we compare it to the internet or open source, where are we? First innings, maybe second innings, I think we’re just getting started. There are so many companies that are dabbling in this, and a meaningful but small percentage of them are getting serious, but that number is starting to grow fast.
As you look forward, are there some technologies or approaches that you’re particularly excited about?
I’ll tell you some of the things that my colleagues identified a year or two ago that we’re really excited about. Container technology for cloud applications, being able to work on a variety of clouds and by extension on the edge is a really powerful theme in the market right now because so many great analytics companies grow up in one cloud. Then of course clients say, ‘Well I’m on another cloud’, or, ‘I want to be multi-cloud’, or, ‘I want to be private cloud', hybrid… or whatever, you’ve got to be able to apply this across a bunch of different storing computer environments. The boundaries between edge and cloud and all that stuff is blurring, there are these great technologies out there now that puts huge amounts of computing power at the edge. So, that whole theme, and there are a lot of really cool announcements this past summer around that from the cloud companies, that whole theme is really pervasive and important.
The other thing we’ve seen which we’re really excited about is, the development of strong horizontal layers in stream processing technology. Forgive me for geeking out here, if you really want to understand what’s happening in a plant, it’s one thing to go back and play with yesterday’s data, which is what most solutions in the past have enabled us to do, but if you want to see what’s happening right now and which of the five or six things that you think might be the problem are actually a problem, you need to be stream-processing that date, you need to have that data flowing through a pipeline, kind of subject to continuous revision and analysis. There are now some really cool technologies around stream processing, that are making that whole platform level of doing that more robust and scalable. We built a bunch of that stuff ourselves four or five years ago, and we’re now reliant on some great open source technologies to do that.
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